Online convex optimization (OCO) was first proposed by Zinkevich [1]}, who showed that projected gradient descent attains sublinear regret bounds in the online setting. OCO generalizes previous online problems like the experts problem  [2]}, and has become widely influential in the learning community [3]}, [4]}.
To the best of our knowledge, Paschos et al. [5]} have been the first to apply the OCO framework to caching. Beside proposing OGD for the single cache, they extend it to a simple networked scenario, where users have access to a set of parallel caches that store pseudo-random linear combinations of the files. They propose no-regret algorithms in both settings. Bhattacharjee et al. [6]} extend this work proving tighter lower bounds for the regret and proposing new caching policies for the networked setting that do not require content coding. We depart from these works in considering OMD algorithms, a more general request process, and allowing for integral caches.
